(Forestry Sciences 76) M. Köhl (auth.), Piermaria Corona, Michael Köhl, Marco Marchetti (eds.)-Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring-Springer Nethe.pdf

ADVANCES IN FOREST INVENTORY FOR SUSTAINABLE FOREST
MANAGEMENT AND BIODIVERSITY MONITORING

Advances in Forest Inventory
for Sustainable Forest
Management and Biodiversity
Monitoring
edited by
Piermaria Corona
University of Tuscia,
Michael Köhl
Dresden University of Technology,
Dresden, Germany
and
Marco Marchetti
University of Palermo,
Palermo, Italy
Viterbo, Italy
SPRINGER-SCIENCE+BUSINESS MEDIA, B.V.

A C.I.P. Catalogue record for this book is available from the Library of Congress.
P.O. Box 322, 3300 AH Dordrecht, The Netherlands.
Cover art: ‘Veduta di Palermo’, 1875, Francesco Lojacono.
Printed on acid-free paper
All Rights Reserved
© 2003
No part of this work may be reproduced, stored in a retrieval system, or transmitted
in any form or by any means, electronic, mechanical, photocopying, microfilming, recording
or otherwise, without written permission from the Publisher, with the exception
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and executed on a computer system, for exclusive use by the purchaser of the work.
ISBN 978-90-481-6466-0 ISBN 978-94-017-0649-0 (eBook)
DOI 10.1007/978-94-017-0649-0
Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 2003
Softcover reprint of the hardcover 1st edition 2003

V
PREFACE
Forests represent a remnant wilderness of high recreational value in
the densely populated industrial societies, a threatened natural resource
in some regions of the world and a renewable reservoir of essential raw
materials for the wood processing industry. In June 1992 the United
Nations Conference on the Environment and Development (UNCED) in
Rio de Janeiro initiated a world-wide process of negotiation with the aim
of ensuring sustainable management, conservation and development of
forest resources. Although there seems to be unanimous support for
sustainable development from all quarters, there is no generally accepted
set of indicators which allows comparisons to be made between a given
situation and a desirable one. In a recent summary paper prepared by the
FAO Forestry and Planning Division, Ljungman et al. (1999) find that
forest resources continue to diminish, while being called upon to produce
a greater range of goods and services and that calls for sustainable forest
management will simply go unheeded if the legal, policy and
administrative environment do not effectively control undesirable
practices. Does the concept of sustainable forest management represent
not much more than a magic formula for achieving consensus, a vague
idea which makes it difficult to match action to rhetoric?
The concept of sustainable forest management is likely to remain an
imprecise one, but we can contribute to avoiding management practices
that are clearly unsustainable.
This book presents selected results of the highly successful conference on
“Collecting and Analyzing Information for Sustainable Forest
Management and Biodiversity Monitoring, with special reference to
Mediterranean Ecosystems”, which was convened in December 2001 by
the Research Group 4.02 of the International Union of Forest Research
Organisations (IUFRO) in Palermo, Italy.
The introductory chapter concerns a comprehensive overview on new
approaches for multiresource forest inventories. This is followed by five
sections covering applications of remote sensing technology, sampling
techniques, landscape pattern and habitat suitability assessment,

VI
information on European forest resources and selected case studies from
other countries and regions all over the world.
The conference itself and this book are a fine example of effective and
focused science networking. The editors, Dr. Piermaria Corona, Dr.
Michael Köhl and Dr. Marco Marchetti, are to be congratulated.
K. von Gadow

VII
TABLE OF CONTENTS
PREFACE
P
P V
E
INTRODUCTION
I
I XIII
N
1. New approaches for multi resource forest inventories 1
M. Köhl
REMOTE SENSING
S
S TECHNOLOGIES
T
T
2. Combining remote sensing and field data for deriving
unbiased estimates of forest parameters over large regions 19
M. Nilsson, S. Folving, P. Kennedy, J. Puumalainen, G. Chirici,
P. Corona, M. Marchetti, H. Olsson, C. Ricotta, A. Ringvall, G.
Ståhl, E. Tomppo
3. Using remote sensing and a spatial plant productivity model
to assess biomass change 33
J.L. Kesteven, C.L. Brack, S.L. Furby
4. Estimating number of Pteridophyte and Melastomataceae
species from satellite images in western Amazonian rain
S. Rajaniemi, E. Tomppo, K. Ruokolainen, H. Tuomisto
5. Computation of a dynamic forest fire risk index by the use of
a long-term NOAA-AVHRR NDVI data set
L. Bottai, R. Costantini, G. Zipoli, F. Maselli, S. Romanelli
forests 57
65

VIII
6. Testing Ikonos and Landsat 7 ETM+ potential for stand-level
forest type mapping by soft supervised approaches
G. Chirici, P. Corona, M. Marchetti, D. Travaglini
7. Use of high resolution satellite images in the forest inventory
and mapping of Piemonte region (Italy)
F. Giannetti, F. Gottero, P.G. Terzuolo
8. Updating forest inventory data by remote sensing or growth
models to characterise maritime pine stands at the
management unit level
J.S. Uva, M. Tomé, J. Moreira, P. Soares
9. Stratification of a forest area for multi source forest
inventory by means of aerial photographs and image
A. Pekkarinen, S. Tuominen
10. Estimating forest canopy structure using helicopter-borne
LIDAR measurement
Y. Hirata, Y. Akiyama, H. Saito, A. Miyamoto, M. Fukuda, T.
Nishizono
SAMPLING
S
S TECHNIQUES
T
T
11. Presence/absence sampling as a substitute for cover
assessment in vegetation monitoring
G. Ståhl
12. A two-phase sampling strategy for forest inventories
L. Fattorini
71
87
97
segmentation 111
125
137
143

IX
13. Assessment of non-wood-goods and services by cluster
M. Scheuber, M. Köhl
LANDSCAPE PATTERN AND HABITAT SUITABILITY
L
L
14. Describing landscape pattern by sampling methods
C. Kleinn, B. Traub
15. Habitat characterization and mapping for umbrella species -
An integrated approach using satellite and field data
R. Löfstrand, S. Folving, P. Kennedy, J. Puumalainen, T. Coch,
B. Kenter, M. Köhl, T. Lämås, H. Petersson, S. Tuominen, C.
Vencatasawmy
16. A multi temporal analysis of habitat suitability
B. Kenter, T. Coch, M. Köhl, R. Löfstrand, S. Tuominen
17. Assessing forest landscape structure using geographic
C. Ricotta, P. Cecchi, G. Chirici, P. Corona, A. Lamonaca, M.
Marchetti
18. Comparison of landscape indices under particular
consideration of the geometric and geographic moving
window concept
M. Köhl, K. Oehmichen
19. Comparative analysis of tourism influence on landscape
structure in Mallorca using remote sensing and socio-
economic data since the 50s
G. Banko, R. Elena, T. Wrbka, C. Estreguil
sampling 157
175
191
205
windows 221
231
245

X
INFORMATION ON
I
I EUROPEAN FOREST RESOURCES
20. Key-attributes for the monitoring of non-timber forest
resources in Europe
W. Abderhalden, T. Coch
21. Mapping forest in Europe by combining earth observation
data and forest statistics
R. Päivinen, M. Lehikoinen, A. Schuck, T. Häme, S. Väätäinen,
K. Andersson, P. Kennedy, S. Folving
22. European Forest Information System – EFIS. A step towards
better access to forest information
P. Kennedy, S. Folving, A. Munro, R. Päivinen, A. Schuck, T.
Richards, M. Köhl, H. Voss, G. Adrienko
STUDIES FROM SELECTED COUNTRIES AND REGIONS
23. Mapping and monitoring of tree resources outside the forest
in Central America
T. Koukal, W. Schneider
24. Monitoring status and condition of Australian
Mediterranean-type forest ecosystems
R. Thackway, M. Wood, C. Atyeo, R. Donohue, B. Allison, R.
Keenan, A. Lee, S. Davey
25. Analysis of the cork forest of Ben Slimane (Morocco) using
multi temporal images
L. Ongaro, G. Ramat
267
279
295
313
325
343

XI
26. Derivation of LAI estimates from NDVI and conventional
data for the simulation of forest water fluxes
M. Chiesi, F. Maselli, M. Bindi
27. Predictive vegetation mapping in the Mediterranean context:
considerations and methodological issues
I.N. Vogiatzakis, A. Malounis, G.H. Griffiths
28. Ideas and options for anationalforest inventory in Turkey
M. Dees, Ü. Asan, A. Yesil
29. Multilevel monitoring systems for cork oak (Quercus suber
L.) stands in Portugal
N.A. Ribeiro, A.C. Gonçalves, S. Dias, T. Afonso, A.G. Ferreira
30. Assessing and monitoring the status of biodiversity-related
aspects in Flemish forests by use of the Flemish forest
inventory data
K. Van Loy, K. Vandekerkhove, D. Van Den Meersschaut
CONCLUSION
C
C
INDEX
I
I
LIST OF REVIEWERS
R
R
EDITORS PROFILES
E
E
353
361
375
395
405
431
435
439
441

XIII
INTRODUCTION
P. Corona, M. Köhl, M. Marchetti
During the 1980`s concern about the deterioration of forests
throughout Europe led to an increasing awareness of the environmental,
cultural, economic and social values of forests. Further impetus to the
process of sustainability develop and protecting forests came from global
efforts at management, conservation and sustainable development related
to all types of forests and forestry, especially the 1992 United Nations
Conference on Environment and Development (UNCED) and the regional
follow-up processes. Today, national forestry programs stimulate and
promote the implementation of the UNCED decisions: the Rio
Declaration, Agenda 21 (in particular, chapter 11 on forests), the “Forest
Principles”, the forest elements of the Conservation on Biological
Diversity (CBD), and the Framework Convention on Climate Change
(FCCC). Regional processes such as the Montreal or the Pan-European
process defined criteria and indicators for sustainable forest management,
which include forest resources, health and vitality, biological diversity as
well as productive, protective and socio-economic functions of forests.
In such a perspective, the conventional principles of forestry have
undergone significant revisions. Environmental and other non-wood
goods and services provided by forest ecosystems gained significant
importance to society during the last decades, both in absolute terms and
relative to wood production (FAO 2001). All over the world, the idea of
sustainable, close-to-nature and multi-functional forestry has
progressively replaced the unbalanced perception of forests as a source for
timber (e.g., see Kohm and Franklin 1997, Corona and Zeide 1999, von
Gadow 2000): this reflects the recognition of the need to consider forests
as integrated ecosystems embedded in a definite tolerance domain, rather
n
than limitless producers of commodities for human consumption.
Sustainable development is based on the harmony of growth processes
among interacting systems, and the concept of sustainable management is
associated with biodiversity. Sustainability and diversity are ecologically
interrelated. Management of a renewable resource, such as forests, is
defined as sustainable when it is utilized within certain eco-biological

XIV
limits. Sustaining wood production does not always mean sustaining the
forest ecosystem. A forest cannot be managed without paying attention to
the efficiency and functionality of the system; this would be neither
scientifically valid nor technically acceptable as a key issue would be
missed: forests are complex biological systems. And if it is true that the
system concept is relatively new in forestry, it is just as true that the
growing awareness of the importance of this concept has led to significant
f
changes in the definitions, goals and limits of forestry, abandoning the
strategy of forest “normalization” (Ciancio et al. 1999). On such premises,
understanding the forest as a whole guides the understanding of its
elements and in turn knowledge of the role of the individual parts helps in
understanding the forest. Holism and reductionism are two sides of the
same coin. One is opposite to and complements the other. The scientific
paradigm is radically different, but the objective is the same: to pursue the
highest level of knowledge of nature (Ciancio and Nocentini 1997).
The change of paradigm has led to new management approaches such as
adaptive management, coactive management, ecosystem management,
management based on the emulation of natural disturbances, or systemic
management (Ciancio et al. 1999, Kimmins 2002). Beyond the
specificities of each paradigm, sound sustainable forest management
answers Society’s needs by first pursuing the goal of the efficiency of the
forest as a biological system and secondly intra- and inter-generational
equity. Emphasising the importance of ecosystem reactions and dynamic
feedback resulting from human intervention, operational focus is
n
n
predominantly shifted from prediction (i.e., ex ante perspective) to
monitoring (i.e., ex post control).
t
Against this background, the sustainable management of the multiple
functions of forest resources requires - compared to traditional practices -
a substantial increase of amount and sensitivity of information for
decision making processes. It is a matter of course that objective decisions
need objective information. What cannot be measured in an objective and
t
unbiased way cannot be effectively managed. Monitoring changes and
transformation processes in ecosystems, which may be of marginal or
structural nature, plays a fundamental role in understanding complex
system reactions to abiotic, biotic and anthropogenic factors. The totality
of direct and indirect forest values (e.g., environmental, historical,
cultural, experimental, didactic, recreational, or landscape) need to be
related to a system structure and organisation (e.g., complexity,
biodiversity, or regeneration ability) over a wide range of ecologically

XV
relevant scales from both the spatial domain (from the tree group, to the
stand, the forest, the landscape) and the time domain (long-term stability,
accounting for catastrophic events and climatic changes).
From a theoretical point of view, determining such properties at various
scales are generally agreed objectives of forest surveys. The
methodological opportunities and feasibility for programs focused on a
comprehensive assessment of forest ecosystem attributes evolving into
global environmental survey programs have been intensively studied, and
are conceptually well shared throughout the world (e.g., Lund 1998;
Corona and Marchetti 2000). The forest research community has been
t
rather active to develop methods and tools for inventorying and
monitoring non-wood goods and services. However, implementation in
operational applications is still quite contradictory and fairly often not
effective.
For instance, sampling frames are different for monitoring the productive
functions or non-wood goods and services of forests. While traditional
forest inventories concentrating on the productive function of forests have
been limited to assessments within forest areas, multiresource surveys
require the assessment of forests in their landscape context. FAO’s Global
Forest Resources Assessment partially supports this issue by extending
the forest area for which results are to be presented from forest area to
“other wooded land”, where forested areas with a tree canopy cover
between 5 and 10 percent are included (FAO 2000). The extension of
forest inventory and monitoring programs to areas outside productive
forests is a major requirement for an integration into other surveys of
natural renewable resources. This holds especially true for the Alpine and
Mediterranean regions, where wooded lands outside productive forests
comprise diverse natural and seminatural environments, such as
abandoned agricultural land, natural pastures, or areas above the
timberline. Forests are dynamically connected to their surrounding areas,
and the spatial and structural composition of border zones as well as the
interconnection of forests to other land cover classes are driving factors
for ecological processes on the landscape level (Forman and Godron
1986).
Information needs originating from the consideration of ecological,
environmental or socio-economic aspects are hardly met by adding some
“new” attributes to existing lists of attributes of traditional and established
forest inventory approaches. Sampling frames have to be extended to
areas outside forests and sampling designs have to be developed that

XVI
widen the scope from timber production to the diverse functions and
services provided by forests. Systems of nomenclature need to be
implemented that capture the entire information potential and utilize
indicators, modelling approaches and attributes that can directly be
assessed within a comprehensive statutory framework driven by the
ongoing national and international processes and programs related to
biodiversity conservation, forest protection, habitat conservation, climatic
changes, or forest externalities.
Including information on non-productive functions of forests in forest
resource assessments renders the provision of spatially explicit data in
mapped format necessary. Traditional sampling-based forest inventories
are able to provide statistical information on a sound background of
sampling theory, but they usually fail in supporting effective estimation
and visualization in the spatial domain. This holds especially true for
spatial information at the local level. Mapping forest attributes and
associated characteristics is fundamental for sustainable multiresource
forest management planning at the stand and landscape level, and mapped
information also represent an essential information source for many tasks
such as the assessment of habitat suitability, recreational potential,
protection from natural hazards or hydrological aspects. The need for
analyzing and providing spatially explicit data can be met by including
remote sensing imagery and GIS. Distinctively, the remote sensing sector
is now poised on the brink of major changes as it has been much improved
by the implementation of very high resolution satellite imagery that may
compete directly with the traditional aerial photography data source.
However, the benefits of remotely sensed data, especially the potential to
perform automated analyses and frequent measurements with relatively
low costs per area unit, should still be considered in parallel with field-
based methods. The combination of both field based and remote sensing
approaches has to be stipulated in order to provide mapped information
with the required thematic resolution.
Any rational decision related to the maintenance and enhancement of the
multiple functions provided by forests needs to be based on objective
information. “Each action is knowledge, and all knowledge is action”
(Maturana and Varela 1998). Forest inventory and monitoring programs
are a key element in providing objective information and are thus an
essential element of any strategy for the management, conservation and
sustainable development related to all types of forests and the entire forest
sector. Methods for data collection should provide cost-efficient, reliable,

XVII
intuitively clear and consistent information for decision processes and
satisfy today’s and future information needs. As forests are complex and
open systems, which are subject to human-induced, biotic and abiotic
dynamics, they will never reach a steady state. Thus, inventorying current
state and monitoring changes is essential for an objective decision process
and for controlling the effect of human interventions and natural
perturbations and dynamics.
References
Ciancio, O., Nocentini, S. 1997. The forest and man: the evolution of forestry thought
from modern humanism to the culture of complexity. Systemic silviculture and
management on natural bases. In Ciancio O. (ed.), The forest and man, Accademia
Italiana di Scienze Forestali, Firenze, Italy, pp. 21-114.
Ciancio, O., Corona, P., Iovino, F., Menguzzato, G., Scotti, R. 1999. Forest management
on a natural basis: the fundamentals and case studies. Journal of Sustainable Forestry
1/2: 59-72.
Corona, P., Zeide, B. (eds.) 1999. Contested issues of ecosystem management. Food
Product Press, Binghampton, USA.
Corona, P., Marchetti, M. 2000. Towards an effective integration of forest inventories and
natural resources surveys: the Italian perspective. In Hansen M., Burk T. (eds.),
Integrated Tools For Natural Resources Inventories In The 21st Century, USDA,
Forest Service, North Central Reseach Station GTR, NC-212, USA, pp. 28-34.
FAO 2000. Global Forest Resources Assessment 2000.FAO Forestry Paper 140, Rome,
Italy.
FAO 2001. State of the forests 2001. FAO Report, Rome, Italy.
Forman, R.T.T., Godron, M. 1986. Landscape Ecology, John Wiley and Sons, New York,
USA.
Gadow von, K., Pukkala, T., Tomè, M. (eds.) 2000. Sustainable Forest Management.
Kluwer Academic Publishers, Dordrecht, Netherlands.
Kimmins, J.P. 2002. Future shock in forestry. The Forestry Chronicle 78: 263-271.
Kohm, K.A., Franklin, J.F. 1997. Creating a forestry for the 21st century. The science of
ecosystem management. Island Press, USA.
Lund, H.G. (ed.) 1998. IUFRO Guidelines for designing multipurpose resource
inventories. IUFRO World Series, Vol. 8, Vienna, Austria.
Maturana, H.R., Varela, F.J. 1998. The Tree of Knowledge. Shambhala, Boston, USA.

CHAPTER 1
NEW APPROACHES FOR MULTI
RESOURCE FOREST INVENTORIES
M. Köhl
Dresden University of Technology, Chair of Forest Biometry and Computer Sciences,
f
Pienner Stasse 8, D - 01737 Tharandt, Germany. Email: koehl@forst.tu-dresden.de
Abstract
In the past the focus of productive functions of forests and rendered the assessment of
information forest resource assessments was put on the assessment of the inside forested
areas sufficient. The need to monitor the sustainability of yield made permanent surveys a
widespread tool in forest surveys. Nowadays the public awareness concerning
environmental issues and forests and the information needs expressed by decision makers
requires a shift from production oriented forest resource assessments towards assessments
that provide information on the multiple functions of forests. Only a limited amount of
information can be provided by adding a set of new attributes to the list of attributes
commonly used in assessing the productive function of forests and utilizing their survey
designs. The diverse information needs that have to be satisfied by multi-resource forest
surveys require the adoption of new assessment and survey approaches and the extension
of assessments from forests to landscapes. This paper reviews a selection of
methodological approaches recently presented for the assessment of the multiple functions
of forests.
1. INTRODUCTION
The philosophy of sustainable forest management was developed in an
environment, where forests were primarily seen as a source of timber. The
idea of sustainability was first mentioned in a Saxonian forest regulation
in the 16th
century (Richter 1963) and became the leading principle of
forest management in the beginning of the 18th
century (Carlowitz 1713).
Sustainability was the key principle for the reconstruction of the
devastated and heavily exploited forests in central Europe and the fight
1
P. Corona et al. (eds.), Advances in Forest Inventory for Sustainable Forest Management
t
and Biodiversity Monitoring, 1-16.
© 2003 Kluwer Academic Publishers.

2
against timber shortage, which had to be faced by households and small
sized industries (Speidel 1972). In the beginning of the 19th
century Hartig
(1804) presented a classical formulation of sustainability by defining
sustainable yield as a percentual yield of wood of commercially useable
quality in yearly or periodic quantities of equal or increasing volume. The
focus on the sustainability of timber supply lead to the development and
implementation of rotation forest systems that resulted in even-aged,
single species and thus homogeneous stands.
At the end of the 19th
century the need for the sustainability of the
h
multiple functions of forest was expressed by Hagen (1867) and taken up
and extended in the 20th
century, e.g. by Dietrich (1941) and Leibundgut
(1949). Nowadays the multifunctional role of forests, especially recreation
and protection, is rated high by the public opinion and became a
significant objective in forest management planning (Bachmann et al.
1998). The idea of multifunctional sustainability of forests is reflected in
many national and international guidelines and programs such as in the
ITTO criteria for the measurement of sustainable tropical forest
management (1992) and the list of criteria and indicators for sustainable
forest management issued by the Ministerial Conference for the Protection
of Forests in Europe (MCPFE 2000).
For many years, forest inventories have been concerned with assessing the
sustainability of the productive function of forests, i.e. the growing stock
of particular areas, the rate of growth of this stock and the extent to which
it is utilized. Since the last decades there has been an increasing demand
for information on other, non-productive forest functions due to the
requests of policy makers and the public’s perception of forests as one of
the last close-to-nature landscape elements. From a survey design
perspective this requires not only the adoption of new sets of attributes,
their nomenclature and measurement rules, but renders the development
of new survey concepts necessary.
2. INFORMATION NEEDS, INVENTORY
OBJECTIVES AND ATTRIBUTES
As the principle of sustainable yield has been replaced by the principle
of sustainable management, management practices as well as information
t
needs that have to be satisfied by forest inventories changed. Information
needs have been variously defined, although one of the most useful

3
sources to derive a general concept of current information needs is the
definition of sustainable management agreed at the Helsinki Inter-
ministerial Conference in 1993. Here sustainable forest management was
defined as “the stewardship and use of forests in a way, and at a rate, that
maintains their biodiversity, productivity, regeneration capacity, vitality
and their potential to fulfill, now and in the future, relevant ecological,
economic and social functions, at local, national, and global levels, and
that does not cause damage to other ecosystems” (MCPFE 2000, p. 39).
For tropical countries guidelines to assess the sustainability of particular
management approaches have been described by the International
Tropical Timber Organisation (ITTO 1992), where the emphasis is on
socio-economic criteria.
A set of clear objectives for multi-resource forest inventories can be
defined. Most of the objectives need information on current state and
change and require thus the use of repeated inventories, which means that
they are essentially monitoring systems. Innes (1995) emphasized that
those monitoring programs or inventories are primarily resource
monitoring tools, whether at the national level or a t a more regional or
even local scale and serve mainly as a tool establishing the compliance
with policy aims. Within the general framework laid out by the MCPFE
and ITTO six core areas can be defined, for which information has to be
provided:
− contribution to global carbon cycles;
− ecosystem health and vitality;
− productive function of forests;
− biological diversity;
− protective functions;
− socio-economic functions and conditions.
Information on each of these core areas cannot be directly assessed and
captured by single attributes. In accordance with the MCPFE these core
areas are termed criteria, which ‘characterize or define the essential
elements or set of conditions or processes by which sustainable forest
management may be assessed’ (MCPFE 1998a). The ‘indicators show
changes over time for each criterion and demonstrate the progress made
towards its specified objective’ (MCPFE 1998b). Within the general
international debate ‘an indicator is a means devised to reduce a large
quantity of data down to its simplest form retaining essential meaning for
t
the questions that are being asked of the data. In short, an indicator is
designed to simplify’ (Ott 1978). Indicators as defined by the MCPFE are

4
for example ‘land use and forest area’, ‘changes in serious defoliation’,
‘total carbon storage’ or ‘share of the forest sector from the cross national
product’. Some of the indicators cannot be captured by the assessment of
forests but need additional data sources such as national statistics.
The criteria and indicators need to be implemented in multi-resource
inventories. Brassel (1995) described the problem of integrating the
assessment of non-productive forest functions in a national forest
inventory program. For the example of the Swiss National Forest
Inventory he shows that not the actual existence of a function but the
potential of providing the function can be assessed. He gives a set of
attributes that provide information on the functional potentials of forests
and can be directly recorded in the field:
− wood production: standing volume, increment, drain, accessibility,
structure, stage of development, age, size of stand;
− biodiversity: number of woody species, especially trees and
shrubs;
− nature protection: forests and forest margins as habitats for flora
and fauna;
− pasturage: traces of other usage, game damage;
− recreational functions: traces of human influence and over-
utilization;
− protective functions: traces of surface erosions, stand density,
surface (bare soil, long grass, litter) as indicators of possible
avalanches, traces of rockfall.
A number of forest functions cannot be assessed from within the forest
itself but only in its environment. Brassel (1995) gives the following
examples:
− avalanche defense;
− flooding;
− wind breaks;
− deadning of noise;
− purity of drinking water;
− protection against extremes
of temperature;
− landscape protection;
− hunting;
− filtering;
− sink for CO2.
Concerning the assessment of the productive function of forests, Innes
(1995) states an opinion that is widely spread inside and outside the forest
society: ‘Many methods have been developed to design, undertake and
analyze such inventories and the problems are well-known.’ However, in
many European countries a gradual transformation of silvicultural

5
practices towards close-to-nature and continuous cover forest systems,
favoring uneven-aged, mixed species and multi-layer stands can be
observed (von Gadow 1995). This transformation affects the design of
permanent forest resource assessments in two ways: (1) the variability of
the population increases and requires higher sampling fractions to obtain a
desired precision and (2) techniques for growth and yield prognosis do no
longer hold, as they are - with few exceptions - applicable for even-aged,
single species stands only. This calls for the development of new survey
approaches for the assessment and monitoring of the productive function
of forests.
For multi-resource inventory systems of nomenclature have to be
developed that capture the information needs and utilize indicators,
modeling approaches and attributes that can directly be assessed. In
addition assessments cannot be restricted to forest areas but have to
incorporate the environment of forests and areas outside forest land.
3. SURVEY SAMPLING AND REMOTE SENSING
APPROACHES FOR MULTI RESOURCE
INVENTORIES
The shift of information needs from productive functions to non-wood
goods and services and ecological aspects of forests initiated the
development of new forest resource assessment approaches. As it would
be beyond the scope of this paper to provide a comprehensive survey of
methods for multi-resource inventories, a selection of approaches utilizing
field assessments and remote sensing will be presented and discussed in
the light of their potential for operational applications.
3.1 Survey methods
Most extensive forest surveys utilize a systematic layout of field
samples (EC 1997). This approach proved effective for the assessment of
attributes describing the productive function of forests, but does not
provide a sufficient tool for many aspects of non-wood goods and services
and the assessment of the complexity of forest ecosystems. To
demonstrate the need for new survey approaches the examples of the

6
assessment of rare species, plant density estimates and coarse woody
debris are given.
3.1.1 Assessment of rare species by adaptive cluster sampling
Monitoring species diversity becomes an important objective in close-
to-nature forest ecosystems. Random or systematic allocation of sampling
units involves the risk to miss rare species. Thompson (1990) describes
adaptive cluster sampling as an efficient method for the assessment of
species with low abundance. Adaptive cluster sampling allocates sampling
units in two steps: (1) an initially fixed number of sample plots is
randomly or systematically distributed over the sampling area; (2) in each
plot, where the rare species under concern is found, the neighboring plots
are measured. In any of these additional plots, where the species of
interest is found, another set of neighboring plots is established. The
procedure is continued until no further plots are found, where the species
of interest occurs. Despite the fact, that a priori sample sizes cannot be
calculated, the procedure is an efficient approach to sample rare species
(Roesch 1993). In a simulation study utilizing computer generated spatial
patterns Ziese (1999) compared adaptive cluster sampling with randomly
and systematically distributed plots and found that adaptive cluster
sampling proved to be superior, if rare species show a low proportion
(<5%) and a clustered spatial pattern.
3.1.2 Plant density estimation
The problem of estimating plant densities commonly arises in forest
surveys when non-tree species are considered. In vegetation surveys
quadrat count methods are a widespread tool, but according to Clayton
and Cox (1986) ‘in many times it is extremely time-consuming or
impratical to map all the events or carry out quadrat sampling’.
Accordingly, distance methods are often preferred to quadrat count
methods. Distance based estimators involve measuring the distance from
randomly selected points to a defined number of neighbors. As distance
methods often fail to find the analytic form for the distance distribution
when processes are non-random, Upton and Fingleton (1985) and Ord
(1990) provided estimators, which are robust to departures from spatial
randomness. However, the practical applicability of these proposals seems
to be limited due to the prohibitive amount of measurements involved.
Patil et al. (1979) established a relation between the plant density and the

7
probability density function of squared point-to-plant distances under
fairly general conditions on plant pattern. On the basis of this relation they
propose a plant density estimator which just requires point-to-plant
distances and which results are consistent and asymptotically normal.
Barabesi and Fattorini (1995) suggested to collect point-to-plant distances
by the use of ranked-set sampling. Patil et al. (1994) give an overview on
ranked set sampling. The method involves the selection of m random
samples with m units in each sample from an infinite population. In the
next step the units in each sample are ranked by visual assessment or any
other procedure that does not require exact measurements. The unit with
the smallest rank is selected from the first sample, the unit with the second
smallest rank from the second sample and this procedure is repeated until
the unit with the highest rank is selected from the m-th sample. The
procedure requires than the quantification of the m units out of the m2
units originally selected. The procedure can be repeated r times in order to
obtain enough quantifications for inference, resulting in n=mr quantified
units out of m2
r selected units. Barabesi and Fattorini (1995) showed for
various spatial plant patterns the improvement on simple random
sampling that can be achieved by estimating plant density by a ranked set
sampling of point-to-plant distances.
3.1.3 Assessment of coarse woody debris by transect relascope
sampling and guided transect sampling
The assessment of coarse woody debris has become an important part
of forest surveys, as dead decaying wood was identified to be important
for many species occurring in forest ecosystems. Ståhl and Lämås (1998)
discuss the performance of circular plot sampling, strip surveying, line
intersect sampling and transect relascope sampling for the assessment of
downed coarse woody debris. While the first three methods are well
known, transect relascope sampling was introduced recently by Ståhl
(1998) and can be considered as a combination of relascope sampling and
line intersect sampling. Along survey lines a wide angle relascope is used
and – according to relascope sampling – all downed logs that appear to be
larger than the relascope angle are tallied. The procedure enables to
estimate the length of downed logs by only counting the logs included in
the sample.
Ringvall and Ståhl (1999) describe a method called guided transect
sampling that elaborates on transect based sampling by including auxiliary

8
information to decide upon the exact route of a surveyor. If the inventory
area is subdivided in quadrats or pixels, a surveyor has the choice to move
to one of the three pixels in front of him. Auxiliary information is used to
assign probabilities to the pixels to be entered next. Information from
remote sensing can be utilised to assign probabilities according to some
PPS-rule to individual pixels and thus determine the route. In sampling
scarce objects the probabilities should be selected in a way that the route
leads to a large number of expected objects.
3.2 Remote sensing
Remote sensing techniques have been described as an ideal tool for
extensive forest surveys, as they provide geo-referenced information at
low cost. The information content of forest stand maps based on remote
sensing data can be increased by combining field assessments and remote
sensing imagery. This approach is especially useful where maps are
required that have to show the spatial pattern of attributes that are either
not directly assessable in remote sensing imagery or that cannot be
assessed with appropriate accuracy. However, in practical applications
two major disadvantages limit the operational application of remote
sensing techniques for the assessment of non-wood goods and services:
(1) the limited number of forest classes that can be derived by multi-
spectral satellite imagery (e.g. forest/non-forest or broadleaved, coniferous
and mixed species forests) and (2) insufficient classification accuracies
(Bodmer, 1993, Kellenberger, 1996). Holmgren and Thuresson (1998)
describe the limited suitability and utilization of satellite remote sensing
for sustainable forest management planning purposes on the strategic,
tactical and operational level.
Since the 1990’s two methods have been described that facilitate the
operational use of remote sensing even at the local level: (1) the kNN-
method and (2) spectral unmixing. The “k nearest neighbour” (kNN)-
method relates terrestrial samples to the spectral information of pixels
(Kilkki and Päivinen, 1987, Tomppo, 1993). For the entire set of pixels
without associated ground assessments the k nearest neighbours in the
spectral image space are determined among those pixels, which coincide
with the location of field samples. The values of attributes assessed on the
ground at the location of the k nearest pixels are weighted by the distances
in the spectral image space and assigned to the respective pixels for which
no ground information is available. Pixel estimates are plotted to produce

9
maps that show the spatial distribution of attributes assessed on the
ground in the resolution of the remote sensing data.
Most European forest ecosystems show small-scale heterogeneity, which
results in a large amount of mixed pixels by the currently available multi-
spectral sensors and their spatial resolutions. By utilizing hyperspectral
remote sensing sensors image data are collected in an enormous number
(i.e. 30 to more than 200) of narrow and adjacent spectral bands. Despite
the fact that hyperspectral imagery is an extension of multispectral
imagery the tools applied for image analysis and interpretation differ from
the well known approaches in multispectral image analysis. A
hyperspectral scene can be seen as an image with a spectrum of gray
values, which are available for each pixel. For a given geographic area the
data can be viewed as a cube, having two dimensions that represent the
spatial position and one that presents wavelengths. The image spectra can
be compared with known spectra from field or laboratory experiments and
enable to detect and map the spectral signatures of objects. This
techniques is known as spectral analysis and utilizes the information of
the entire spectral image space and searches for characteristics of spectra
that are similar to the known spectra of objects. An alternative technique
for analyzing hyperspectral data is called spectral unmixing. Spectral
unmixing assumes that the reflectance of a pixel in an individual spectral
band is a linear combination of the spectral reflectance of different objects
or – in the nomenclature of hyperspectral image analysis – endmembers.
The resulting spectra are thus a composite of the endmembers or pure
spectra of objects in a pixel, weighted by their area proportion. Köhl and
Lautner (2001) found for a test site in the Ore Mountains, Germany that
spectral mixture analysis provided good results for the assessment of
mixture proportions of deciduous and coniferous trees. Classification of
stand types, using the maximum likelihood algorithm, provided good
results especially for further differentiation of tree species groups and for
mapping of natural age classes.
4. ASSESSMENTS OF FORESTS IN A LANDSCAPE
CONTEXT
Many forest functions cannot be described by assessing data inside
forests only, but need to look at forests in their landscape context. This
requires to describe the transition zone between forests and other land

10
cover classes, i.e. the forest margin, and to quantify the spatial distribution
of forest patches at the landscape level.
Figure 1. Transect of forest margin (source: Brändli et al. 1995).
4.1 Assessment of the forest margin
According to Forman and Godron (1986), the primary significance of
shape in determining the nature of patches in a landscape appears to be
related to the edge effect. An edge is the outer band of a patch, where the
environment is significantly different to the interior of the patch. The edge
effect is related to the different species composition and abundance found
at the edge. Forest edges can be described by various attributes such as
length, width, shape, vertical and horizontal structure, density or interior-
to-edge ratio (Figure 1).
Brändli et al. (1995) describe an assessment procedure for the forest edge
that was operationally applied in the 2nd
Swiss national forest inventory.
d
In the Swiss NFI sample plots are distributed in a systematic grid.
Whenever a forest margin lies within 25 m of the center of a field plot an
assessment of the forest margin is conducted. A taxation line of 50 m
length forms the basis for the forest edge assessment. Determination of
t

11
botanical diversity, description of important habitats for various animals,
especially birds and insects and the judgment of the aesthetic value for
recreational purposes are important objectives of the edge assessment.
b
Results of the forest edge survey are given by Brändli and Ulmer (1999).
4.2 Landscape indices
Landscape ecology focuses on three characteristics of the landscape:
(1) structure, (2) function and (3) change (Forman and Godron, 1986).
Thus, landscape ecology involves the study of landscape patterns. A patch
is defined as a nonlinear surface area differing in appearance from its
surroundings. In addition patches are often embedded in a surrounding
area or matrix that has a different composition (Forman and Godron
1986). Landscape indices are a widely used tool to quantify spatial
landscape structures. The many landscape indices that have been
described in the literature can be grouped into the following eight classes
(McGarigal and Marks 1994):
− area metrics;
− patch density metrics;
− edge metrics;
− shape metrics;
− core area metrics;
− nearest neighbour;
− diversity metrics;
− contagion and interpersion metrics.
The calculation of one index for an entire landscape does often fails to
reflect the structure of landscapes. Thus the application of the moving
windows technology has been suggested in the context of landscape
analysis. Windows (synonyms are Kernels, masks or filters) have their
origin in image analysis and are used to characterise spatial information in
a neighbourhood. Sub-areas are separated from the area under concern
and analysed. According to Merchant (1984) and Dillworth et al. (1994)
windows of fixed size – so called geometric windows - show
disadvantages in situations where not the neighbourhood of pixels but the
t
neighbourhood of patches has to be analysed. For these situations
Merchant (1984) proposed to use geographic windows, where based on an
initial geometric window the window is expanded until all patches
covered by the initial geometric window are included. Ricotta et al. (1998)

12
and Oehmichen (2001) compare geometric and geographical windows in a
landscape analysis context.
5. MODELING SPECIES HABITATS
Landscape indices have often been proposed by the remote sensing and
GIS community, while ecologists tend to put more emphasise on studying
the functions and processes within the landscape. While landscape indices
provide a quantification of landscape structures they often fail in valuating
landscapes and adding qualitative information. Qualitative information is
indispensable when landscapes are studied in the context of habitat
evaluation. Habitat evaluation procedures are used to provide information
on the potential of landscapes to provide habitats for a species. They result
in maps that show species habitats and are useful for ecological
assessments, conservation planning or estimating the impacts of
management activities (Gray et al. 1996, Rand and Newman 1998). For
example, species requiring specific habitats would benefit from targeted
management prescriptions while species with a narrow geographic
distribution would benefit from a protected area.
In the scope of multi-resource forest inventories habitat evaluation
procedures utilizing habitat modeling are a tool to assess the potential
diversity of forested landscapes. The US GAP analysis program provides
an operational framework for utilising data from different scales (field
data and image data) simultaneously (Jennings 2000). GAP was
envisioned as a national and global land-use planning process to identify
and maintain biological diversity in a set of core biodiversity management
areas (Noss 1999, Scott et al. 1983). The program utilizes landscape scale
analysis of spatially explicit data, which includes large-area vegetation
maps, vertebrate species distributions, and land stewardship maps.
Habitat modeling was studied within EC-JRC’s DMMD (Developing
methods for monitoring diversity as a Contribution to Sustainable Forest
Management in Europe) – project. The combination of geo-referenced
data obtained by remote sensing, field data and information on the habitat
requirements of species was used to model the potential of a landscape to
form habitats for selected key species. If maps that show the habitat
potentials for a set of umbrella species are integrated in GIS multiplayer
operations provide information on the potential biological diversity of
landscapes. Using habitat modeling in a time series context showed the
potential of the approach to distil areas, where habitat potentials for

13
individual species improved or decreased. Kenter et al. (2002) provide
more details on the methodology and its application in a test site.
6. DISCUSSION
While forest inventories concentrating on the productive function of
forests were limited to assessments within forest areas multi-resource
forest surveys require to describe forests in their landscape context.
Adding some “new” attributes to existing lists of attributes and depending
on established survey sampling approaches is not adequate and will fail to
satisfy the current information needs expressed by the public and by
decision makers. Driven by national and international processes and
programs the forest inventory community was active to develop new
methods and approaches for inventorying and monitoring non-wood
goods and services, from which this paper presents only a very limited
number. While the challenge has been taken it should be kept in mind that
forest inventories have to provide information that is intuitively clear.
Acknowledgements
In writing the current paper I benefited from countless discussions with my former
colleagues at the Forest Inventory Department, Swiss Federal Institute for Forest, Snow
and Landscape Research, Birmensdorf, Switzerland. Among those I want to express my
special thanks to Dr. Peter Brassel, Urs-Beat Brändli and Dr. Markus Keller. In addition I
want to thank my PhD-students and research fellows Bernhard Kenter, Katja Oehmichen,
Wolfgang Stümer and Helge Ziese, who were involved in studies I refer to in this paper
and who forced me into rewarding discussions – it is a pleasure to have them around. And
– last but not least – I want to thank Dr. Matthias Scheuber and Dr. Thomas Coch, for the
many discussions we had when collaborating in research projects on non-timber forest
surveys.
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REMOTE SENSING
S
S TECHNOLOGIES
T
T

CHAPTER 2
COMBINING REMOTE SENSING AND
FIELD DATA FOR DERIVING
UNBIASED ESTIMATES OF FOREST
PARAMETERS OVER LARGE
REGIONS
M. Nilsson(1)
, S. Folving(2)
, P. Kennedy(2)
, J. Puumalainen(2)
, G. Chirici(3)
,
P. Corona(3)
, M. Marchetti(3)
, H. Olsson(1)
, C. Ricotta(3)
, A. Ringvall(1)
, G.
Stahl(1)
, E. Tomppo(4)
(1) Swedish University of Agricultural Sciences (SLU), Department of Forest Resource
management and Geomatics, SE-90183 Umeaa, Sweden; Fax: +46-(0)90-778116
(2) Joint Research Center – Institute for Environment and Sustainability, European
Commission; TP 262, I - 21020 Ispra (Va), Italy; Fax: +39-0332-789469.
(3) Italian Academy of Forest Sciences (AISF), piazza Edison 11, 50133 Firenze, Italy,
Fax +39-055-575724
(4) Finnish Forest Research Institute (METLA), Unioninkatu 40 AFIN-00170 Finland;
Fax: +258-9-85705717
Abstract
Remote sensing data can be combined with field data to estimate forest variables over
large regions. The accuracy of these estimates depends, for example, on how well the field
measurements can be linked to satellite images and on how well forest areas can be
identified. In practice, it is difficult to delineate forest areas from other land cover classes;
this fact might cause biased estimates. In this study, a post-stratification approach was used
to combine field data and satellite data to derive unbiased estimates of forest parameters
over large regions. Images from Landsat TM and Terra MODIS were used in combination
with field data from the National Forest Inventory in Northern Sweden. The results show
that the standard deviation for estimates of total stem volume, stem volume for deciduous
m
trees, and dead wood were reduced with 48%, 33%, and 23%, respectively, by using post-
stratification based on Landsat TM data instead of field data alone. A significant
improvement of the estimation accuracy was obtained also when using MODIS data.
19
t
© 2003

20
1. INTRODUCTION
The use of satellite data offers a great potential to derive estimates of
t
both spatial and non-spatial indicators that can be monitored over time
using images from different dates. Methods such as maximum likelihood
classification and regression techniques can be used to map and describe
the forest landscape. However, there is a number of problems that must be
taken into consideration when combining satellite images and field data
for mapping and monitoring purposes. Problems related to image
geometry, positioning of field-plots, mixed pixels, atmospheric
conditions, etc. will affect the possibility to derive precise and unbiased
estimates of forest indicators. In theory, most of these problems can be
handled, but in practice it might difficult to guarantee that the estimates
will be unbiased.
Geometric errors in an image in combination with the positioning errors
of field plots will affect how accurately field data and satellite images can
be matched, which in turn affects the estimation or classification accuracy.
Approaches that are based on small field plots that are linked to pixels
from sensors like Landsat TM will in most cases result in a low accuracy
on a pixel level, partly due to the matching problem, especially if the local
variation in the forest landscape is high. The estimation or classification
accuracy is also influenced by the fact that the correl
f
f ation for satellite
spectral data versus parameters such as stem volume or basal area is
stronger in young stands than in old stands (e.g., Franklin 1986, Horler
and Ahern 1986, Peterson and Nilson 1993).
Digital land cover maps are often used to separate different land cover
d
classes from each other. One problem with map data is that their
definitions seldom agree with the definitions used in a specific inventory.
It is common to use different forest definitions in different applications.
For example, national and international forest inventories often use
slightly different definitions of forest. Thus, maps containing more than
one forest definition are needed. An alternative way to identify forested
areas is to classify images. However, some forest types are, from a
spectral point of view, very close to other land cover classes; this will
affect the classification accuracy in a negative way. Problems related to
mixed pixels will also affect the classification accuracy. Consequently, in
practice it can be difficult to obtain an accurate forestland classification as
a basis for deriving forest-related information.

21
Altogether, there are many problems that, theoretically, can be handled
but in practise are very difficult to deal with. A straightforward approach
to obtain unbiased estimates for large areas is to use post-stratification for
the combination of satellite data and field plots from existing inventories,
such as plots from the ICP Forests network or National Forest Inventories.
Some problems, like geometric errors, still affect the accuracy for
estimates. However, the influence is much smaller in a polygon-based
stratification approach than for pixel-wise estimates. Field plots located on
the boundary between two strata (or two classes) might be assigned wrong
classes due to geometric problems when matching image and field data.
The problem with boundary plots increases when the landscape is divided
into smaller and more detailed classes, both spectrally and spatially. Less
detailed classes will, on the other hand, lead to a higher within class
variation.
The main objective of this study was to investigate to what extent the
estimation accuracy for important forest parameters can be improved
using post-stratification compared to basing the estimates on field data
only. The possibility to use both Landsat TM (20-30 m pixels) and Terra
MODIS data (500 m pixels) in combination with field plots from the
Swedish NFI was evaluated.
y are
Study are
are
ea
ea
ea
y are
64o
N
o
o
Figure 1. Study area.

22
2. MATERIAL AND METHODS
2.1 Test Area
A study area located in the northern part of Sweden at latitude 64°15'N
and longitude 20°50'E was used (Figure 1). Scots pine (Pinus sylvestris)
and Norway spruce (Picea abies
(
( ) dominate the forests in this region.
2.2 Field data
Field plots from the Swedish NFI were used for the post-stratification.
The NFI is carried out as a systematic field sample, where plots are laid
out in square clusters (Ranneby et al. 1987). A cluster in the study region
consists of either 12 temporary plots of 7 m radius or 8 permanent plots of
10 m radius (Figure 2). Permanent plots are re-measured after 5-10 years.
The position of each NFI plot was recorded using GPS receivers.
600 m
500 m
1200 m
1500 m
10 m
7 m
Permanent cluster Temporary cluster
Figure 2. NFI clusters in the study area.

23
2.3 Image classification
Two classifications were made based on a Landsat TM image from
year 2000. Both were made by first dividing the image into segments
using the t-ratio segmentation algorithm presented by Hagner (1990). In
the next step, each segment was assigned a class based on estimates of
mean stem volume and proportion of broadleaf trees. In the first
classification the minimum segment size was set to 1 ha (Landsat, 1ha)
while in the second it was set to 5 ha (Landsat, 5ha). The class definitions
used are presented in Table 1. Note that there is only one class for
broadleaf forests, since there were only a few segments (on the 5 ha level)
with a stem volume greater than 50 m3
/ha, and there were no NFI plots
located within these segments.
Table 1. Class definitions use on segments from the TM image.
Class Stem volume, m3
ha-1
Prop. of broad-leaved trees
1 < 10 -
2 10-50 < 50%
3 50-100 < 50%
4 100-150 < 50%
5 150-200 < 50%
6 200- < 50%
7 >10 > 50%
The pixel-wise estimates were derived using the k Nearest Neighbour
(kNN) method (e.g., Tomppo 1993), in which forest parameters (v) are
calculated as weighted averages of the k nearest field plots (Equation 1).
The feature space distance (d) between a field plot and a pixel defines
d
d
how close they are to each other. Feature space distances can be measured
by arbitrary metrics. In this study, the Euclidean distance was used in the
TM spectral space. The weights (w) used were proportional to the inverse
squared distance (Equation 2). This is essentially an inverse distance
weighted averaging method, as commonly used also for spatial
interpolation (Isaaks and Srivastava 1989).
v v
p j p
j
k
j p
=
∑ , p j ,
1
(1)
where

24
j p
j p i p
k
w
d d
j p i
i
p i
p i
= ∑
1 1
k
∑
2 2
d
∑ , d d d
p p k p
d d
1
d
d p d
, p p k
p k
p d
d
d
d
d
d (2)
dj
d
d ,p
, = feature space distance from pixel p to plot j, and
vj
v ,p
, = variables for the plot with distance dj
d
d ,p
, .
Results by e.g. Tokola et al. (1996) and Nilsson (1997) show that the root
mean squared error of the estimates (RMSE) usually decreases when k is
k
increased, to a certain maximum value. For example, in Swedish studies,
it has been recommended that a suitable number for k in forest inventory
k
is between 5 and 10 (Nilsson 1997). The value of k affects also how
k
strong the averaging effect will be, i.e. tendency towards the mean. The
higher the value used for the k, the more averaging occurs in the
estimates. Further, the smaller the number of field plots are, the smaller
value for k should be used to avoid excessive averaging. Thus, the optimal
k
value of k is a trade-off between the accuracy of the estimates and the
variation retained in the estimates. In this study, k was set to five. The
k
scaling of the different bands in the feature space will affect the estimation
accuracy. For stem volume, Nilsson (1997) has shown that it is more
efficient to use Euclidean distance compared to use Mahalanobis distance
when calculating the feature space distances based on Landsat TM digital
numbers. It was indicated that the estimation accuracy for some variables
might increase if band dependent weights are used. The results also
showed that no scaling of the TM bands is needed when estimating stem
volume.
The estimation of volume and tree species was made separately for areas
inside and outside of forests as according to existing land-cover maps,
using NFI plots as ground truth.
Once all segments had been assigned a class, adjacent segments of the
same class were merged. The final average segment sizes obtained, using
a minimum segment size of 1 ha and 5 hectares, were 11.7 ha and 18.5 ha,
respectively.
The MODIS image (500 m pixels) was classified into 5 classes based on
spectral clustering of band 1 (Red), 2 (NIR), and 4 (Green). Preferably,
the same number of classes as used for Landsat TM should have been
used. However, it was found that that some of the classes became very
small in terms of area coverage when adding more than 5 classes. The
average polygon size after merging adjacent pixels with the same class
was 154.4 ha.

25
A
B
C
Figure 3. Stratification with small segments (1 ha minimum size, A), large segments (5 ha
minimum size, B), and MODIS pixels (C).

26
The clustering was done using the ISODATA algorithm in
ERDAS/Imagine. Before the post-stratification could be carried out,
clouded areas were identified and removed.
As shown in Figure 3, the stratification based on MODIS is much coarser
than the ones derived from Landsat TM. It can also be seen that the 1 ha
minimum segment size produced a more detailed map than the 5 ha limit.
2.4 Post-stratification
The study was limited to the part of Västerbotten county covered by
the Landsat TM scene. The part of the three stratifications located within
this area was extracted and stored as three separate maps. For each map,
the area per stratum was calculated and used in the post-stratification.
Three sets of NFI plots were prepared using ArcInfo, each set
corresponding to one of the three stratifications. In the NFI, plots located
on a stand boundary or on a boundary between different land-use classes
are divided into sub-plots. This will not cause any problems as long as the
entire plot is located within a segment. If a divided plot is located on the
boundary between two adjacent segments from different classes, it could
be that not all sub-parts belong to the same class. In this study, all parts of
a plot were assigned to the same class based on the location of the centre
point of the plot. Divided plots located on the boundary between classes
might therefore increase the within-class variation.
In the NFI, the parameter in a specific stratum is estimated as a weighted
average of two independent samples: one based on permanent clusters and
one on temporary clusters. The weight given to a specific parameter
estimate in each sample is inversely proportional to its variance. For both
permanent and temporary clusters, the parameter estimates for a specific
stratum and corresponding variances were calculated according to
Equations 3 and 4.
h
h
n
j
h
j
n
j
h
j
h
h R
Ah
a
y
A
Yh
ˆ
ˆ
1
1
=
⋅
h Ah
A
∑
∑
=
= , (3)
where
h
j
y = the sum of the values for all plots in cluster j belonging to
stratum h,

27
h
j
a = the total area covered by all plots in cluster j belonging to
stratum h, and
h
A = the total area covered by stratum h, according to the map
used for stratification.
)
ˆ
(
)
ˆ
( 2
h
h
h ) R
Var
A
Y
Var (
)
( h ) (4)
)
ˆ
( h
R
Var( was calculated according to standard methods for ratio estimators
(e.g., Thompson 1992). The total values for the entire area were then
calculated as the sum over all strata. It was assumed that plots in different
t
strata were independent, although this might not always be the case for
plots within the same NFI cluster.
Table 2. Estimates of total stem volume, total area, and proportion of tree species based on
stem volumes.
Method Landuse class Total stem vol
(Million m3
)
Area
(Million ha)
Prop. pine
(%)
Prop. spruce
(%)
Prop. deciduous
(%)
NFI Forestland 115,045 0,989 50,6 34,7 14,7
Agricultural area 0,056 0,063 - - -
Bog 2,401 0,153 78,6 8,6 12,7
Bare rock 1,316 0,048 86,3 7,4 6,3
Other 0,173 0,055 55,5 0,4 44,1
Total 118,991 1,308 51,5 33,8 14,7
Landsat,
1ha
Forestland 108,083 0,965 51,2 33,9 15,0
Bog 2,521 0,162 78,0 8,7 13,3
Bare rock 1,319 0,048 86,7 7,5 5,8
Other 0,160 0,057 54,6 0,5 44,9
Total 112,147 1,299 52,2 32,9 14,9
Landsat,
5ha
Forestland 110,054 0,965 50,5 34,4 15,1
Bog 2,474 0,160 77,9 8,8 13,3
Bare rock 1,349 0,048 86,6 7,3 6,2
Other 0,163 0,055 56,3 0,5 43,2
Total 112,648 1,298 34,0 52,1 13,9
MODIS* Forestland 125,939 1,092 51,1 34,2 14,8
Bog 3,312 0,191 76,5 8,4 15,1
Bare rock 1,242 0,046 85,1 7,6 7,3
Other 0,213 0,053 55,0 7,8 37,1
Total 130,828 1,457 52,0 33,2 14,8
*) Note that the cloud free area is slightly larger for MODIS than for Landsat.

28
3. RESULTS
Area estimates and total values for different land cover classes were
calculated using NFI plots only (named NFI in all Tables) and for post-
stratification based on Terra MODIS and Landsat TM. The estimates of
total stem volume, total area, and proportion of tree species based on stem
volumes are presented in Table 2.
As shown in Table 3 and 4, the standard deviation for all parameters
decreased when post-stratification was used in comparison to using the
field measurements only. The results also indicate that a higher spatial
resolution (minimum segment size) improves the accuracy. It is notable
that the standard deviation obtained for post-stratification based on
MODIS data was substantially lower than the one obtained without any
post-stratification. Especially so, since the pixel size was 500m and the
classification was not optimised for the parameters to be estimated.
Table 3. The total amount of stem volume for all tree species, deciduous trees, and dead
wood and corresponding standard deviations.
Total Deciduous Dead wood
Method Volume
(Million m3
)
Stdev (%) Volume
(Million m3
)
Stdev (%) Volume
(Million m3
)
Stdev (%)
NFI 115,045 5,4 16,948 9,2 3,840 11,5
MODIS* 125,939 3,6 18,580 6,7 4,241 9,5
Landsat, 5ha 110,054 2,9 16,646 6,6 3,731 8,5
Landsat, 1ha 108,083 2,8 16,179 6,6 3,705 8,3
Table 4. The total forest area and the area covered with deciduous forest and
corresponding standard deviations.
Total Deciduous
Method Area (ha) Stdev (%) Area (ha) Stdev (%)
NFI 988955 2,6 134410 11,5
MODIS* 1091553 1,7 162185 8,6
Landsat, 5ha 965475 1,5 152395 6,5
Landsat, 1ha 964875 1,5 161477 5,8

29
We also investigated how the number of sample plots affects the variance.
Figure 3 shows the standard deviation for total stem volume and dead
wood, respectively, as a function of the number of plots measured in the
field survey. Figure 3 and 4 are based on the more detailed post-
stratification that were derived from the TM image (Landsat, 1ha).
Figure 3. The effect on the standard deviation for total stem volume and dead wood using
different sample sizes in combination with the post-stratification based on the Landsat TM
classification with 1 ha minimum segment size.
4. DISCUSSION
The segmentation splits an image into more or less homogeneous
segments or patches based on the spectral values. When running the
segmentation it is important to use a proper minimum segment size. A
small minimum segment size results in small homogeneous segments. If a
large minimum segment size is used it will result in a high within-segment
variation. This means that different forest types could exist within a
segment. Another problem related to segment size is the number of plots
located on a boundary between two classes. If a plot is located on a
boundary, it is difficult to decide to which class it belongs. Geometric
errors will affect the class assignment in a negative way. It is even more
difficult to assign correct classes to sub-parts of divided plots located on a
boundary. The effects of problems of these kinds could not be quantified
in the study; however, the 1ha minimum segment size generated slightly
better estimates than these obtained with a 5 ha limit. This indicates that it
is more important to create homogeneous segments than to focus on the
total boundary length when using post-stratification.
0
1
2
3
4
5
6
500 1000 1500 200 2500 300
Number of plots
Stdev
(%)
0
2
4
6
8
10
12
14
16
500 100 1500 2000 2500 3000
Number of plots
Stdev
(%)
Total volume Dead
Dead

30
One general problem when using satellite images is how to obtain cloud
free images. Sensors like Terra MODIS are attractive to use since they
cover large areas and because the sensor registers images over a specific
area much more frequently than for example Landsat TM. This means that
the possibility to cover a region with cloud free images is much higher for
MODIS than for Landsat TM. Using MODIS, or similar sensors, it is
possible to cover a country with images from just one year. This is
normally not possible using Landsat TM or similar sensors. The fact that
MODIS is viewing a 2330 km wide area in the across track direction and
that it monitors the same point every 1-2 days makes it very attractive to
use in large scale applications. Post-stratification based on MODIS (or
similar sensors) therefore is of great interest although the results show that
the precision will be better with Landsat TM data.
The classes used for post-stratification based on MODIS data relate to
thresholding of spectral signatures in bands 2, 3, and 4, and not directly to
stem volume and tree species. It is therefore likely that the obtained
accuracies can be improved. One way to achieve this would be to give
weights to individual bands depending on how correlated they are with
important forest parameters.
It is important that the relationship between pixel values and forest
conditions are independent of the location, as has been pointed out by
Kilkki and Päivinen (1987). Otherwise, field plots from outside the
estimation area cannot be used without the risk of obtaining biased
estimates. Thus, it might be necessary to do the post-stratification by
r
r
regions or eco-zones.
The estimation accuracy for all parameters is much higher when field
plots and satellite images are combined using post-stratification,
compared to using field plots only. The gain in using post-stratification
might be overestimated due to the way variances for all parameters were
calculated. It was assumed that plots within a NFI cluster were
independent when they belonged to different strata. This is probably not
entirely true. On the other hand, the calculation of variances was based on
the assumption that all NFI clusters were randomly located within the
area. This results in an overestimation of the variances since the NFI is
carried out as a systematic field sample. The total effect of these two
assumptions is not possible to tell from the results in this study. It is
therefore important to further investigate how the variances should be
estimated.

31
A conclusion from the study is that post-stratification is a straightforward
and efficient approach to combine remote sensing and existing networks
of field data, and that this technique avoids most of the problems that may
cause bias when the other kinds of combinations are applied. The
conclusion is supported by previous studies by Dees (1996) and Hansen
and Wendt (1999).
Acknowledgements
This study was carried out under a contract (16182 – 2000 – 05 F1ED ISP SE) with the
Joint Research Centre of the European Commission.
This paper has been carried out also with the financial support from the Commission of the
European Communities, Agriculture and Fisheries (FAIR) specific RTD programme,
CT98-4045, “Scale dependent monitoring of non-timber forest resources based on
indicators assessed in various data sources”. The content of this paper does not represent
the views of the Commission or its services and in no way anticipates the Commission’s
future policy in this area.
References
Dees, M. 1996. Regressions- und Kleingebietsschätzung bei forstlichen
Grossraumsinventuren unter Nutzung von Forsteinrichtungs- und Satellitendaten.
Mitteilungen der Abteilung für Forstliche Biometrie 96-1. Albert-Ludwigs-Universität.
Freiburg, Germany.
Franklin, J. 1986. Thematic mapper analysis of coniferous forest structure and
composition. International Journal of Remote Sensing 7:1287-1301.
Hagner, O. 1990. Computer Aided Forest Stand Delineation and Inventory Based on
Satellite Remote Sensing. In The Usability of Remote Sensing for Forest Inventory
and Planning, pp. 94-105. Edited by R. Sylvander. SNS/IUFRO workshop, Umeå.
Hansen, M.H., Wendt, D.G. 1999. Using classified Landsat thematic mapper data for
stratification in a statewide forest inventory. In Proceedings of the first annual forest
inventory and analysis symposium. Edited by McRoberts, R.E., Reams, G.A., and Van
Deusen, P.C. USDA General Technical Report NC-213.
Horler, D.N.H., Ahern, F.J. 1986. Forestry information content of Thematic Mapper data.
International Journal of Remote Sensing 7: 405-428.
Kilkki, P., Päivinen, R. 1987. Reference sample plots to combine field measurements and
satellite data in forest inventory. In Remote Sensing-Aided Forest Inventory. Seminars
organised by SNS and Taksaattoriklubi, Hyytiälä, Finland. pp. 209-212.
Nilsson, M. 1997. Estimation of Forest Variables Using Satellite Image Data and Airborne
Lidar. Doctoral thesis. Swedish University of Agricultural Sciences, Umeå.
Peterson, U., Nilson, T. 1993. Successional reflectance trajectories in northern temperate
forests. International Journal of Remote Sensing 14: 609-613.
Ranneby, B., Cruse, T., Hägglund, B., Jonasson, H., Swärd, J. 1987. Designing a new na-
tional forest survey for Sweden. Studia Forestalia Suecica, No. 177.
Thompson, S.K. 1992. Sampling. John Wiley & Sons: New York. pp 59-70.

32
Tokola, T., Pitkänen, J., Partinen, S., Muinonen, E. 1996. Point accuracy of a non-
parametric method in estimation of forest characteristics with different satellite
materials. International Journal of Remote Sensing 17: 333-2351.
Tomppo, E. 1993. Multi-Source National Forest Inventory of Finland. In Proceedings of
Ilvessalo Symposium on National Forest Inventories, pp. 52-59. August 17-21,
Finland.

CHAPTER 3
USING REMOTE SENSING AND A
SPATIAL PLANT PRODUCTIVITY
MODEL TO ASSESS BIOMASS
CHANGE
J.L. Kesteven(1)
, C.L. Brack(2)
, S.L. Furby(3)
(1)
(
( Na
N
N tional Carbon Accounting Tea
T
T m, Austral
t
t ian Greenhouse Of
O f
f
f i
f
f ce, GP
G
G O Box 621
Ca
C
C nberra
r
r ACT Australia 2601; Fax: +61-2-62
6
6 741381; email:
Jenny.Kesteven@g
@
@ ree
g
g nhouse.gov.au.
(2) Sch
(
( ool of Resources, Environment and So
S
S ciety, Australian National Univers
r
r ity,
t
t
Ca
C
C nberra
r
r ACT Australia 0200; Fax: +61-2-61253535; email:
Cr
C
C is.Brack@anu.edu
d
d .au.
(3)
(
( Mathe
M
M matical and Inf
n o
f
f rmation Scie
S
S nces, Co
C
C mmonwealth
t
t Scientific
S
S and Indu
d
d strial
Research Org
r anisat
g
g ion, We
W
W mbley,
l
l WA
W
W , Australia, 6014; email:
Suz
S
S anne.Fu
F
F rby
b @csiro.au
Abstract
Accounting for biomass and carbon change in forestry and agriculture under the Kyoto and
other international protocols requires an assessment of the change in land cover, including
afforestation, reforestation and deforestation events. Due to the time associated with soil
carbon and biomass decay, the impact of an event associated with land cover change may
continue over many years. Remote sensing was used to identify the location, area and time
of an afforestation, reforestation or deforestation event. This time-based, activity-by-
activity approach, covering all continental woody vegetation, provides a platform of land
cover history. This land cover history is used in conjunction with calculations of Net
Primary Productivity and estimates of pool turnover and decay to provide a first phase
estimate of biomass and carbon on a spatially referenced basis. The Net Primary
Productivity was calculated for Australia using a physiological model (3-PG (Spatial))
based on the relationship between the photosynthetically active radiation absorbed by plant
canopies (APAR) and the (biomass) productivity of those canopies at a monthly time step.
The factor converting APAR to biomass was reduced from the selected optimum value by
modifiers dependent on soil fertility; atmospheric vapour pressure deficits, soil water
content and temperature. Leaf Area Index, essential for the calculation of APAR, was
estimated from 10-year mean values of Normalized Difference Vegetation Indices.
Incoming short-wave radiation - and hence APAR - was corrected for slope and aspect
using a Digital Elevation Map. The ESOCLIM package was used to generate climate
33
t
© 2003 Kluwer Academic Publishers.

34
surfaces for the country. Soil fertility and water holding capacity values were obtained
from the (digital) soil atlas of Australia. The correlation between the first phase estimate of
biomass and sites across Australia that ranged from arid shrublands to tall wet sclerophyll
(2 – 450 t/ha biomass) was examined. This correlation is significant and is useful for
improving the efficiency of estimating biomass and carbon totals and change.
1. INTRODUCTION
The Australian Government became a signatory to the United Nations
Framework Convention on Climate Change in 1992 and the Kyoto
Protocol in 1997 (Commonwealth of Australia 2000). The Kyoto Protocol
requires an estimate of the quantity of carbon emitted or sequestered from
forests during the reference period (1990) and the commitment period
between 2008 and 2012. Much of this carbon change is associated with
land cover change-afforestation, reforestation and deforestation. Land
cover change introduces a long period of change as soil carbon and
biomass decay over many years following deforestation and biomass is
sequestered at variable rates after afforestation or reforestation. Multi-
temporal land-cover-change analyses were used to identify the area,
location and timing of clearing (or disturbance) events between 1972 and
2000. To estimate the biomass at the time of clearing it was important to
understand the rates of growth of various vegetation types in addition to
the time of clearing and age since last disturbance or clearing.
This presentation outlines the methods adopted by the Australian
Greenhouse Office to estimate the extent, location and timing of
deforestation and reforestation events. Further, the methods to estimate
the net primary productivity are also presented. These estimates of land
cover change and productivity can be used to estimate biomass and other
non-woody resources on a spatial basis over the whole of the Australian
continent.
2. REMOTE SENSING
Landsat TM and MSS imagery were the principal sources of remotely-
sensed data considered for the 1970–2000 study period.
Landsat TM data has been available since 1987. Data sources such as
radar and airborne scanner data were excluded because of their limited
availability during the study period. Although aerial photographs are more
widely available, they were not considered as a primary data source

35
because of the prohibitively high cost of analysis. However, they were
incorporated into the Q&A analysis. NOAA AVHRR imagery was not
considered because the pixel size (1.1 km) was too coarse for the
detection of areas subject to change required at the sub-hectare scale for
Kyoto compliance, and the archive does not provide for consistently
available imagery.
The remote sensing analysis was divided into several stages (Figure 1):
Figure 1. Land Cover Change Program Conceptual Framework.
− scene identification and acquisition;
− year 2000 Australia mosaic;
− registration and calibration of individual scenes to the year 2000
base;
− mosaicing of the individual scenes for each time slice for
1:1,000,000 map sheet regions;
− thresholding analysis to produce maps of woody vegetation cover
at each time slice; and.
− attribution of directly human-induced land use change.
A complete description of all remote sensing methodologies and
techniques used is included in Furby (2001).
To assess the pattern of land cover change across Australia for the period
1970 to 2000 an understanding of the cyclic nature of change that occurs

36
every few years was required. For this reason the following dates were
chosen for analysis; early 1972, 1977, 1980, 1985, 1988, 1989, 1991,
1992, 1995, 1998 and 2000.
Table 1. Table of scenes selected (Total = 3348).
YEAR Sensor Used Number of images
1972 MSS 285
1977 MSS 194
1980 MSS 345
1985 MSS 307
1988 MSS 308
1989 TM 321
1991 TM 311
1992 TM 301
1995 TM 304
1998 TM 302
2000 ETM+ 369
2.1 Scene identification and acquisition
The best images were those that were completely free of any problems.
The most common of these problems included, but are not limited to, data
errors (eg line drop-out), cloud, smoke and extensive flooding. Preference
in the image selections was given to same-date sequences along paths and
to temporal consistency of the image dates selected. Optimal image dates
were those closest to 1 January in each time slice except for the 1989 time
slice where the preferred date was December 31.
The year 2000 registration and calibration base has a full national
coverage, while the preceding image sequences omit those areas of
Australia not able to support some form of woody vegetation (forests,
shrubland, etc). It was a requirement in the scene selections that all the
images be available in digital format.

37
2.2 Year 2000 Australia mosaic
Figure 2. The Year 2000 Australia mosaic.
The Year 2000 Australia mosaic provides a single base image to which
images from earlier years can be matched without having to adapt the
procedures to accommodate the shifting scene centre locations. It was
formed from 369 Landsat 7 ETM+ scenes from July 1999 to September
2000. There were three steps in the process of creating the base image:
rectification to a map grid; calibration to create a radiometrically
consistent base to which the images from other dates will be corrected;
and mosaicing into 1:1,000,000 map sheet tiles which were used in all
subsequent analyses. There are thirty seven map sheet tiles across the
country.

38
2.3 Rectification and Registration
The aim of the rectification procedure was to produce a geographically
d
d
consistent base across the continent to which the images from other dates
were corrected. Although seeking to create a base that is as accurate as
possible in an absolute sense, it is the relative accuracy of the rectification
of the images to each other that determines the limitations of the land
cover change detection.
A viewing-geometry approach with block adjustment was used to ortho-
rectify the Landsat 7 ETM+ images. The approach involved: importing
the raw image data; selecting ground control points to link the image data
to the map base; selecting tie points to link overlapping images to each
other; fitting the viewing geometry model to relate image line and pixel
coordinates to map northing and easting coordinates; and resampling the
image.
The viewing–geometry approach required a height from a DEM as well as
a map coordinate (northing and easting) for each control point. A
combination of the AUSLIG 9 Second and 3 Second DEMs was used.
This mix of DEMs is the best consistently available DEM over the
continent. The DEM was also required for the full image area during the
resampling step in the processing.
Map coordinates for the ground control points have been obtained from
two sources. The Queensland Department of Natural Resources (QDNR)
supplied DGPS coordinates and location information (so that the features
could be identified in the images) for numerous features across
Queensland. Where these features could be confidently located in the
Landsat 7 ETM+ images they were viewed as the most accurate ground
control points available. The other source of map coordinates for the
ground control points was raster versions of the AUSLIG 1:100,000 map
series.
The images from the remaining time slices were registered to the year
2000 base using the same viewing-geometry approach. The ground
control points for registration were automatically matched to the year
2000 base image using image correlation. The image matching technique
is described in Campbell (1999).

39
2.4 Calibration
The calibration procedure used the year 2000 data to produce a
radiometrically consistent base across the continent to which the images
from other dates could be corrected. The calibration of the year 2000 base
consisted of correction to scaled top-of-atmosphere reflectance and
correction for surface reflectance properties.
The calibration of the remaining images to the year 2000 base consisted of
two stages. In the first stage the same physical corrections were applied to
the images as were applied to the images forming the year 2000 base. The
parameters for these corrections are considered to be well known for the
more recent Landsat 5 TM data and Landsat 7 ETM+ data. The
appropriate processes and parameter values for Landsat MSS are less well
understood. In the second stage of the calibration process invariant targets
were used to compare the corrected overpass image to the base image. If
the images were not well matched radiometrically, the comparison also
provided a linear correction to ensure a match. The invariant target
correction is described in Furby and Campbell (2001). This correction
compensates for the less certain parameter estimates in the physical
corrections for the Landsat MSS images.
2.4.1 Correction to scaled top-of-atmosphere reflectance
Correction to scaled top-of-atmosphere reflectance was performed by
correction for sensor and on-ground gains and offsets, then correction for
sun angle and earth-sun distance.
The gain and offset correction was applied to each image band and the
gain and offset for each image band were obtained from the report file
supplied with the raw image. The solar zenith angle for each pixel and the
distance from the scene centre to the sun were calculated, based on the
image location and acquisition date and time.
2.4.2 Correction for surface reflectance properties
Correction for surface reflectance properties was performed by
application of a combination of two simple bi-directional reflectance
distribution function (BRDF) kernels using common kernel coefficients.
Simple variations of Walthall’s model, described in Danaher et al. (2001),
were used. The model has three parameters which were calculated by

40
solving equations based on the overlap areas of the Landsat 7 ETM+
images. The same parameter values were applied to all images.
2.4.3 Invariant target correction
After the above corrections had been applied to each of the images
from 1972 to 1998, a set of invariant targets was collected to compare
each corrected image to the base image. Robust regressions were used to
estimate the linear corrections (gain and offset) to match each image to the
base image using the pixel intensities from the invariant targets. Typically
the same targets were used for each image from a particular path/row
sequence, with some minor modifications if there were significant patches
of cloud or smoke in a particular image.
2.5 Mosaicing
Prior to performing the thresholding, or vegetation analysis, the
individual images for each time slice were mosaiced to the 1:1,000,000
map sheets. The individual images have different extents in each time
slice that creates numerous edge effects if the analyses were to be
performed on the individual images. Mosaicing the images over a
common area simplifies the thresholding significantly.
A set of rules was specified to determine the order of overlay of data in
overlap areas to minimise seasonal, atmospheric and on-ground factors
that would affect the analysis of the mosaiced data. Vector files containing
the boundaries of each image date within the mosaics were created so that
the acquisition date of each image pixel within the mosaic could be
identified.
2.6 Thresholding Specifications
The analyses required the production of maps of woody vegetation
cover for each time slice and hence maps of land cover change. Indices
that discriminate between woody and non-woody cover were derived.
Thresholds were used to assign a probability of woody cover to each
image pixel based on these index values. Multi-temporal processing was
applied to create the final woody cover and change products.
The outputs from the land cover change analysis are maps of woody
vegetation cover for each time slice. Areas of change were identified by

41
comparing the maps from consecutive time slices. Clearing and re-
vegetation events were defined as changes from woody cover to non-
woody cover, or the reverse, in the woody cover maps. Implicit in this
definition is a woody density threshold below which the cover was
considered to be non-woody. This threshold was fixed at approximately
twenty- percent cover in the remote sensing analyses.
This land cover history is used in conjunction with calculations of Net
Primary Productivity and estimates of pool turnover and decay to provide
a first phase estimate of biomass and carbon on a spatially referenced
basis.
2.6.1 Stratification
Variations in woody vegetation type, other predominant land cover
types, soil, geology and rainfall all contribute to the discrimination
between woody and non-woody cover. No single index or index-pair
provided adequate discrimination between woody and non-woody cover
over the whole of Australia. The analysis area was divided into
stratification zones within which there was little or no variation in the
factors that affect the discrimination between woody and non-woody
cover.
The datasets that were used to perform this stratification included soil,
f
f
vegetation and climate maps, land use patterns and terrain variations. An
initial stratification based on these datasets was performed to identify the
regions for which separate sets of ground-truth information were supplied.
Further stratification is performed by the thresholding process in
combination with inspection of the images and analysis of the training site
data. The index derivation and threshold setting were performed
separately within each stratification zone.
2.6.2 Index derivation
Training sites (homogeneous areas with known ground cover type)
were used to derive indices that discriminate between woody and non-
woody cover. A number of training sites were required to cover the full
range of cover types and densities within the woody and non-woody
cover.
Canonical variate analyses were performed using the training data to
derive suitable indices. A canonical variate analysis (CVA) finds the

(Forestry Sciences 76) M. Köhl (auth.), Piermaria Corona, Michael Köhl, Marco Marchetti (eds.)-Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring-Springer Nethe.pdf

(Forestry Sciences 76) M. Köhl (auth.), Piermaria Corona, Michael Köhl, Marco Marchetti (eds.)-Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring-Springer Nethe.pdf

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